We observe that spike firing rates of neurons depend on the image presented to the AlexNet CNN. Therefore, energy and reliability improvement strategies based on design-time analysis with training examples may not be optimal when they are applied at run-time to process in-field data.
Therefore, in addition to cluster placement when admitting a machine learning model to hardware, NeuroXplorer also supports monitoring key performance statistics collected from the hardware during model execution.
Such statistics can uncover bottlenecks, allowing improving system-level metrics such as energy [1]} and circuit aging [2]}, [3]} through remapping of the neurons and synapses to the hardware.
